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2008


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Effect of quantity and configuration of attached bacteria on bacterial propulsion of microbeads

Behkam, B., Sitti, M.

Applied Physics Letters, 93(22):223901, AIP, 2008 (article)

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[BibTex]

2008


[BibTex]


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Interfaces in confined Ising models: Kawasaki, Glauber and sheared dynamics

Smith, T. H. R., Vasilyev, O., Abraham, D. B., Maciolek, A., Schmidt, M.

{Journal of Physics: Condensed Matter}, 20(49), 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Critical fields of an exchange coupled two-layer composite particle

Goll, D., Kronmüller, H.

{Physica B}, 403, pages: 1854-1859, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Coercivity and domain structure of nanograined Fe-C alloys after high-pressure torsion

Protasova, S. G., Straumal, B., Dobatkin, S. V., Goll, D., Schütz, G., Baretzky, B., Mazilkin, A. A., Nebrasov, A. N.

{Journal of Materials Science}, 43, pages: 3775-3781, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Epitaxial growth and properties of (001)-oriented TbBaCo2O6-αfilms

Kasper, N. V., Wochner, P., Vigliante, A., Dosch, H., Jakob, G., Carstanjen, H. D., Kremer, R. K.

{Journal of Applied Physics}, 103, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Magnetic properties of exchange-coupled L10-FePt/Fe composite elements

Goll, D., Breitling, A., Macke, S.

{IEEE Transactions on Magnetics}, 44(11):3472-3475, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Colloidal cobalt-doped ZnO nanorods: synthesis, structural, and magnetic properties

Büsgen, T., Hilgendorff, M., Irsen, S., Wilhelm, F., Rogalev, A., Goll, D., Giersig, M.

{Journal of Physical Chemistry C}, 112(7):2412-2417, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Raman studies of hydrogen adsorbed on nanostructured porous materials

Panella, B., Hirscher, M.

{Physical Chemistry Chemical Physics}, 10, pages: 2910-2917, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Thermal evolution and grain boundary phase transformations in severely deformed nanograined Al-Zn alloys

Straumal, B., Valiev, R., Kogtenkova, O., Zieba, P., Czeppe, T., Bielanska, E., Faryna, M.

{Acta Materialia}, 56(20):6123-6131, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Hydrogen storage properties of Pd nanoparticle/carbon template composites

Campesi, R., Cuevas, F., Gadiou, R., Leroy, E., Hirscher, M., Vix-Guterl, C., Latroche, M.

{Carbon}, 46, pages: 206-214, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Reversible transformation of a grain-boundary facet into a rough-to-rough ridge in zinc

Straumal, B. B., Gornakova, A. S., Sursaeva, V. G.

{Philosophical Magazine Letters}, 88(1):27-36, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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A library for locally weighted projection regression

Klanke, S., Vijayakumar, S., Schaal, S.

Journal of Machine Learning Research, 9, pages: 623-626, 2008, clmc (article)

Abstract
In this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our code supports multi-threading, is available for multiple platforms, and provides wrappers for several programming languages.

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link (url) [BibTex]

link (url) [BibTex]


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Preface to the Journal of Micro-Nano Mechatronics

Dario, P., Fukuda, T., Sitti, M.

Journal of Micro-Nano Mechatronics, 4(1-2):1-1, Springer-Verlag, 2008 (article)

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[BibTex]

[BibTex]


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A legged anchoring mechanism for capsule endoscopes using micropatterned adhesives

Glass, P., Cheung, E., Sitti, M.

IEEE Transactions on Biomedical Engineering, 55(12):2759-2767, IEEE, 2008 (article)

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Project Page [BibTex]

Project Page [BibTex]


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The hole is important! The quest for ferromagnetism in doped ZnO

Tietze, T., Gacic, M., Schütz, G., Jakob, G., Brück, S., Goering, E.

{BESSY Highlights 2007}, pages: 14-15, 2008 (article)

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[BibTex]

[BibTex]


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Limitations of a simple quantum mechanical model: Magnetic dichroism in a relativistic one-electron atom

Rodr\’\iguez, J. C., Kostoglou, C., Singer, R., Seib, J., Fähnle, M.

{Physica Status Solidi (B)}, 245(4):735-739, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Impact of irradiation-induced point defects on electronically and ionically induced magnetic relaxation mechanisms in titano-magnetites

Walz, F., Brabers, V. A. M., Kronmüller, H.

{Physica Status Solidi (A)}, 205(12):2934-2942, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Polarization selective magnetic vortex dynamics and core reversal in rotating magnetic fields

Curcic, M., van Waeyenberge, B., Vansteenkiste, A., Weigand, M., Sackmann, V., Stoll, H., Fähnle, M., Tyliszczak, T., Woltersdorf, G., Back, C. H., Schütz, G.

{Physical Review Letters}, 101, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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X-ray spectroscopic investigations of Zn0.94Co0.06O thin films

Mayer, G., Fonin, M., Voss, S., Rüdiger, U., Goering, E.

{IEEE Transactions on Magnetics}, 44(11):2700-2703, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Experimental realization of graded L10-FePt/Fe composite media with perpendicular magnetization

Goll, D., Breitling, A., Gu, L., van Aken, P. A., Sigle, W.

{Journal of Applied Physics}, 104, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Hard magnetic L10 FePt thin films and nanopatterns

Breitling, A., Goll, D.

{Journal of Magnetism and Magnetic Materials}, 320, pages: 1449-1456, 2008 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Spin-reorientation transition in Co/Pt multilayers on nanospheres

Eimüller, T., Ulbrich, T. C., Amaladass, E., Guhr, I. L., Tyliszczak, T., Albrecht, M.

{Physical Review B}, 77, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Non-destructive compositional analysis of historic organ reed pipes

Manescu, A., Fiori, F., Giuliani, A., Kardjilov, N., Kasztovszky, Z., Rustichelli, F., Straumal, B.

{Journal of Physics: Condensed Matter}, 20, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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An advanced magnetic reflectometer

Brück, S., Bauknecht, S., Ludescher, B., Goering, E., Schütz, G.

{Review of Scientific Instruments}, 79, 2008 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Optimization strategies in human reinforcement learning

Hoffmann, H., Theodorou, E., Schaal, S.

Advances in Computational Motor Control VII, Symposium at the Society for Neuroscience Meeting, Washington DC, 2008, 2008, clmc (article)

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PDF [BibTex]

PDF [BibTex]


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Dynamic modeling of stick slip motion in an untethered magnetic microrobot

Pawashe, C., Floyd, S., Sitti, M.

Proceedings of Robotics: Science and Systems IV, Zurich, Switzerland, 2008 (article)

pi

[BibTex]

[BibTex]


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Frequency analysis with coupled nonlinear oscillators

Buchli, J., Righetti, L., Ijspeert, A.

Physica D: Nonlinear Phenomena, 237(13):1705-1718, August 2008 (article)

Abstract
We present a method to obtain the frequency spectrum of a signal with a nonlinear dynamical system. The dynamical system is composed of a pool of adaptive frequency oscillators with negative mean-field coupling. For the frequency analysis, the synchronization and adaptation properties of the component oscillators are exploited. The frequency spectrum of the signal is reflected in the statistics of the intrinsic frequencies of the oscillators. The frequency analysis is completely embedded in the dynamics of the system. Thus, no pre-processing or additional parameters, such as time windows, are needed. Representative results of the numerical integration of the system are presented. It is shown, that the oscillators tune to the correct frequencies for both discrete and continuous spectra. Due to its dynamic nature the system is also capable to track non-stationary spectra. Further, we show that the system can be modeled in a probabilistic manner by means of a nonlinear Fokker–Planck equation. The probabilistic treatment is in good agreement with the numerical results, and provides a useful tool to understand the underlying mechanisms leading to convergence.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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In situ observation of cracks in gold nano-interconnects on flexible substrates

Olliges, S., Gruber, P. A., Orso, S., Auzelyte, V., Ekinci, Y., Solak, H. H., Spolenak, R.

{Scripta Materialia}, 58(3):175-178, 2008 (article)

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[BibTex]

[BibTex]


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Transmission electron microscopy study of the intermixing of Fe-Pt multilayers

Kaiser, T., Sigle, W., Goll, D., Goo, N. H., Srot, V., van Aken, P. A., Detemple, E., Jäger, W.

{Journal of Applied Physics}, 103, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Spin state and orbita moments across the metal-insulator-transition of REBaCo2O5.5 investigated by XMCD

Lafkioti, M., Goering, E., Gold, S., Schütz, G., Barilo, S. N., Shiryaev, S. V., Bychkov, G. L., Lemmens, P., Hinkov, V., Deisenhofer, J., Loidl, A.

{New Journal of Physics}, 10, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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A crucial role for primary cilia in cortical morphogenesis

Willaredt, M. A., Hasenpusch-Theil, K., Gardner, H. A. R., Kitanovic, I., Hirschfeld-Warneken, V. C., Gojak, C. P., Gorgas, K., Bradford, C. L., Spatz, J. P., Wölfl, S., Theil, T., Tucker, K. L.

{The Journal of Neuroscience}, 28(48):12887-12900, 2008 (article)

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[BibTex]

[BibTex]


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Exchange coupled composite layers for magnetic recording

Goll, D., Macke, S., Kronmüller, H.

{Physica B}, 403, pages: 338-341, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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XMCD studies on Co and Li doped ZnO magnetic semiconductors

Tietze, T., Gacic, M., Schütz, G., Jakob, G., Brück, S., Goering, E.

{New Journal of Physics}, 10, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Desorption studies of hydrogen in metal-organic frameworks

Panella, B., Hönes, K., Müller, U., Trukhan, N., Schubert, M., Pütter, H., Hirscher, M.

{Angewandte Chemie International Edition}, 47, pages: 2138-2142, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Wetting transition of grain-boundary triple junctions

Straumal, B. B., Kogtenkova, O., Zieba, P.

{Acta Materialia}, 56, pages: 925-933, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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Time-resolved X-ray microscopy of spin-torque-induced magnetic vortex gyration

Bolte, M., Meier, G., Krüger, B., Drews, A., Eiselt, R., Bocklage, L., Bohlens, S., Tyliszczak, T., Vansteenkiste, A., Van Waeyenberge, B., Chou, K. W., Puzic, A., Stoll, H.

{Physical Review Letters}, 100, 2008 (article)

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DOI [BibTex]

DOI [BibTex]


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The Gilbert equation revisited: anisotropic and nonlocal damping of magnetization dynamics

Fähnle, M., Steiauf, D., Seib, J.

{Journal of Physics D}, 41, 2008 (article)

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DOI [BibTex]

DOI [BibTex]

2005


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Kernel Methods for Measuring Independence

Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.

Journal of Machine Learning Research, 6, pages: 2075-2129, December 2005 (article)

Abstract
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound near independence on the Parzen window estimate of the mutual information. Analogous results apply for two correlation-based dependence functionals introduced earlier: we show the kernel canonical correlation and the kernel generalised variance to be independence measures for universal kernels, and prove the latter to be an upper bound on the mutual information near independence. The performance of the kernel dependence functionals in measuring independence is verified in the context of independent component analysis.

ei

PDF PostScript PDF [BibTex]

2005


PDF PostScript PDF [BibTex]


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A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1935-1959, December 2005 (article)

Abstract
We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

ei

PDF [BibTex]

PDF [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O., Schölkopf, B.

Journal of Computer and System Sciences, 71(3):333-359, October 2005 (article)

Abstract
In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We propose several embeddings and recall that an isometric embedding in a Banach space is always possible while an isometric embedding in a Hilbert space is only possible for certain metric spaces. As a result, we obtain a general maximum margin classification algorithm for arbitrary metric spaces (whose solution is approximated by an algorithm of Graepel. Interestingly enough, the embedding approach, when applied to a metric which can be embedded into a Hilbert space, yields the SVM algorithm, which emphasizes the fact that its solution depends on the metric and not on the kernel. Furthermore we give upper bounds of the capacity of the function classes corresponding to both embeddings in terms of Rademacher averages. Finally we compare the capacities of these function classes directly.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Selective integration of multiple biological data for supervised network inference

Kato, T., Tsuda, K., Asai, K.

Bioinformatics, 21(10):2488 , October 2005 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Assessing Approximate Inference for Binary Gaussian Process Classification

Kuss, M., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1679 , October 2005 (article)

Abstract
Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace‘s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace‘s method.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Banerjee, A., Dhillon, I., Ghosh, J., Sra, S.

Journal of Machine Learning Research, 6, pages: 1345-1382, September 2005 (article)

Abstract
Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.

ei

PDF [BibTex]

PDF [BibTex]


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Support Vector Machines for 3D Shape Processing

Steinke, F., Schölkopf, B., Blanz, V.

Computer Graphics Forum, 24(3, EUROGRAPHICS 2005):285-294, September 2005 (article)

Abstract
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parameters can be automatically set using standard machine learning methods. The surface approximation is based on a modified Support Vector regression. We present applications to 3D head reconstruction, including automatic removal of outliers and hole filling. In a second step, we build on our SV representation to compute dense 3D deformation fields between two objects. The fields are computed using a generalized SVMachine enforcing correspondence between the previously learned implicit SV object representations, as well as correspondences between feature points if such points are available. We apply the method to the morphing of 3D heads and other objects.

ei

PDF [BibTex]

PDF [BibTex]


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Fast Protein Classification with Multiple Networks

Tsuda, K., Shin, H., Schölkopf, B.

Bioinformatics, 21(Suppl. 2):59-65, September 2005 (article)

Abstract
Support vector machines (SVM) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced Lanckriet et al (2004). In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n^3), and produces a dense matrix of size n x n. We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similarly to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Iterative Kernel Principal Component Analysis for Image Modeling

Kim, K., Franz, M., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9):1351-1366, September 2005 (article)

Abstract
In recent years, Kernel Principal Component Analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the Kernel Hebbian Algorithm which iteratively estimates the Kernel Principal Components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics. In spite of this, both super-resolution a nd denoising performance are comparable to existing methods.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Phenotypic characterization of chondrosarcoma-derived cell lines

Schorle, C., Finger, F., Zien, A., Block, J., Gebhard, P., Aigner, T.

Cancer Letters, 226(2):143-154, August 2005 (article)

Abstract
Gene expression profiling of three chondrosarcoma derived cell lines (AD, SM, 105KC) showed an increased proliferative activity and a reduced expression of chondrocytic-typical matrix products compared to primary chondrocytes. The incapability to maintain an adequate matrix synthesis as well as a notable proliferative activity at the same time is comparable to neoplastic chondrosarcoma cells in vivo which cease largely cartilage matrix formation as soon as their proliferative activity increases. Thus, the investigated cell lines are of limited value as substitute of primary chondrocytes but might have a much higher potential to investigate the behavior of neoplastic chondrocytes, i.e. chondrosarcoma biology.

ei

Web [BibTex]

Web [BibTex]


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Local Rademacher Complexities

Bartlett, P., Bousquet, O., Mendelson, S.

The Annals of Statistics, 33(4):1497-1537, August 2005 (article)

Abstract
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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Learning the Kernel with Hyperkernels

Ong, CS., Smola, A., Williamson, R.

Journal of Machine Learning Research, 6, pages: 1043-1071, July 2005 (article)

Abstract
This paper addresses the problem of choosing a kernel suitable for estimation with a Support Vector Machine, hence further automating machine learning. This goal is achieved by defining a Reproducing Kernel Hilbert Space on the space of kernels itself. Such a formulation leads to a statistical estimation problem similar to the problem of minimizing a regularized risk functional. We state the equivalent representer theorem for the choice of kernels and present a semidefinite programming formulation of the resulting optimization problem. Several recipes for constructing hyperkernels are provided, as well as the details of common machine learning problems. Experimental results for classification, regression and novelty detection on UCI data show the feasibility of our approach.

ei

PDF [BibTex]

PDF [BibTex]